
Goodhart’s Law Is Coming for AI
AI success isn’t measured by how much you use it, but by the value it creates. Good metrics drive better decisions; bad metrics drive better scores.
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Articles filed under Digital Transformation.

AI success isn’t measured by how much you use it, but by the value it creates. Good metrics drive better decisions; bad metrics drive better scores.

Most AI projects won’t fail because the technology isn’t capable—they’ll fail because organisations skip the fundamentals. As the era of AI hype gives way to one of accountability, success will depend on trusted data, strong governance, clear business outcomes, and measurable ROI. The real winners won’t be those talking about AI the loudest, but those building the foundations that allow it to deliver lasting business value.

The traditional 'buy don't build' advice served CIOs well for two decades. AI-assisted development, citizen developers and cloud platforms have fundamentally changed that equation.

AI may be saving hours across your organisation, but your best employees could be quietly spending a large portion of those hours fixing its mistakes

AI is making IT spending more visible, more variable, and harder to justify without measurable business outcomes.

SAP’s biggest challenge may not be AI itself, but maintaining customer trust, execution clarity and realistic transformation paths amid growing complexity.

Many CIOs are now balancing executive AI expectations against employee fatigue, operational reality and the growing risk of transformation theatre.

Many organisations claim to be doing AI, but most are still operating at the earliest stages of maturity with isolated tools, fragmented workflows and limited operational integration.

Privacy erosion, AI pilot shutdowns, and workforce compression are converging into the next phase of enterprise AI reality.

Why delaying IPv6 adoption increases cost, complexity and risk—and why organisations should begin their transition now.